Some governments have begun to procure and test artificial intelligence systems that can pursue goals through connected actions. However, publicly documented evidence of mature agentic AI in public administration remains limited, with most initiatives remaining at procurement, pilot, or early deployment stages. This Perspective introduces the democratic authorization gap, defined as a break or attenuation in the demonstrable chain connecting legally and democratically grounded public authority to actions selected, sequenced, or executed by an AI agent. Drawing on democratic delegation, accountability, administrative law, and recent agentic-AI scholarship, the article distinguishes this prospective, authority-based problem from responsibility gaps and technical authorization. It identifies four mechanisms through which the gap may develop: mandate translation, recursive delegation, action diffusion, and contestability lag. Five governance conditions are proposed for the pilot stage: bounded authorization, permission inheritance, action-level traceability, named institutional responsibility, and operational interruption with reversible redress. The argument is anticipatory rather than empirical. Following the Collingridge dilemma, limited evidence before large-scale deployment provides a reason to establish governance conditions while institutional choices remain open.
Mohammed Salah, Fadi Abdelfattah, Aisha Al Araimi et al.· Frontiers in Political Scien...· 0 citations
This study examines how technology readiness shapes logistics professionals’ intention to adopt generative artificial intelligence (AI), with particular attention to the roles of trust, intrinsic motivation, and anthropomorphism. Drawing on the Technology Readiness Index and Self-Determination Theory, the study proposes that optimism, discomfort, insecurity, and service awareness influence adoption intention primarily through trust-based motivational and perceptual pathways. A cross-sectional survey was conducted among 203 logistics practitioners in Oman, Saudi Arabia, and Iraq, and the data were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings show that technology readiness dimensions do not directly predict generative AI adoption intention but significantly shape trust in AI. Trust emerged as a central mechanism, exerting a strong direct effect on adoption intention while also positively influencing intrinsic motivation and anthropomorphism. In turn, both intrinsic motivation and anthropomorphism significantly enhance intention to adopt generative AI. The indirect results further indicate that technology readiness operates mainly through trust-driven pathways rather than as a direct behavioral driver. The study extends AI adoption research by repositioning technology readiness as an upstream psychological resource and by highlighting the importance of trust, motivation, and perceptions of human-like AI in the logistics context.
A. Hamid, Mohammed Salah, Adam Y. A. Hamad et al.· Emerging Science Journal· 0 citations
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